electricsheepafrica/africa-health-facilities-ghana

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health-facilities
- hxl
- gha
pretty_name: "Ghana Healthsites"
dataset_info:
splits:
- name: train
num_examples: 1734
- name: test
num_examples: 433
---

# Ghana Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/ghana-healthsites) · **License:** `ODbL` · **Updated:** 2025-10-15

---

## Abstract

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-10-15. Geographic scope: **GHA**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 2,168 |
| **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) |
| **Train split** | 1,734 rows |
| **Test split** | 433 rows |
| **Geographic scope** | GHA |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -2.9733–1.1949), `y` (range 4.8838–10.9796), `osm_type` (node, way), `amenity` (pharmacy, hospital, clinic).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 154173029.0–13208372240.0), `name` (Ernest Chemist, Drug Store, Clinic), `changeset_id` (range 7911073.0–173278687.0), `uuid` (4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–46.875), `healthcare` (pharmacy, hospital, clinic), `changeset_version` (range 1.0–11.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-health-facilities-ghana")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 24.2% | -2.9733 – 1.1949 (mean -0.721) |
| `y` | float64 | 24.2% | 4.8838 – 10.9796 (mean 6.2805) |
| `osm_id` | int64 | 0.0% | 154173029.0 – 13208372240.0 (mean 5928268646.9723) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 46.875 (mean 12.8142) |
| `amenity` | object | 2.5% | pharmacy, hospital, clinic |
| `healthcare` | object | 47.6% | pharmacy, hospital, clinic |
| `name` | object | 15.3% | Ernest Chemist, Drug Store, Clinic |
| `changeset_id` | int64 | 0.0% | 7911073.0 – 173278687.0 (mean 110439476.5807) |
| `changeset_version` | int64 | 0.0% | 1.0 – 11.0 (mean 1.7911) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | 4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -2.9733 | 1.1949 | -0.721 | -0.2449 |
| `y` | 4.8838 | 10.9796 | 6.2805 | 5.6588 |
| `osm_id` | 154173029.0 | 13208372240.0 | 5928268646.9723 | 6542893586.5 |
| `completeness` | 6.25 | 46.875 | 12.8142 | 12.5 |
| `changeset_id` | 7911073.0 | 173278687.0 | 110439476.5807 | 119505116.0 |
| `changeset_version` | 1.0 | 11.0 | 1.7911 | 2.0 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 23 column(s) with >80% missing values were removed: `operator`, `source`, `speciality`, `operator_type`, `operational_status`, `opening_hours`.... 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from Global Healthsites Mapping Project and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `x`, `y`, `healthcare`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ghana-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_ghana,
title = {Ghana Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/ghana-healthsites},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*